We compiled the variables used in the Social Vulnerability Index
(SoVI; Cutter et al. 2003) and the Heat Vulnerability Index (HVI;
Harlan et al. 2013) from multiple sources for 358 census tracts that
were either within or intersected with the City of Phoenix, Arizona
boundary. The HVI we used was developed by Harlan et al. (2013) to
best capture the variables known from extensive prior research to
influence heat vulnerability in Phoenix. Harlan et al. (2013) adapted
their index based on the nationwide HVI first created by Reid et al.
(2009). We converted all variables, as needed, to summary values
(means, medians, or percentages) rather than raw totals because of the
variation in population size between census tracts. For comparability,
all variables were from data collected in the year 2016 or ending in
2016.
To compute SoVI, we compiled 27 census tract level variables from the
2012-2016 5-year American Community Survey (ACS) using the tidycensus
package (version 0.9) in R (version 3.5.1). Since initial publication
by Cutter et al. (2003), the variables used in SoVI have been
partially modified to reflect the evolution of researchers’
understanding of social vulnerability in the literature. In this
study, we used variables identified in the most recent iteration
(2010-2014) of SoVI, which can be found online at University of South
Carolina’s Hazards & Vulnerability Research Institute (HVRI)
webpage (HVRI, 2014). Because it was not available in the 2012-2016
ACS, we replaced the variable "percent of population living in a
nursing facility" from the most recent SoVI iteration with the
variable "percent of population with a disability".
To calculate HVI, we followed a version of HVI that was modified by
Harlan et al. (2013) to capture heat vulnerability in Maricopa County
(which encompasses Phoenix). Specifically, we obtained 10 census tract
level variables related to heat vulnerability. In an identical fashion
to the methods for obtaining ACS data in SoVI, we calculated seven
variables from the 2012-2016 5-year ACS. We obtained one variable,
residential central air conditioning prevalence, from the Maricopa
County Assessor’s Office for the year 2016.
The remaining two HVI variables, vegetated area mean and standard
deviation, we collected by calculating the normalized difference
vegetation index (NDVI; Tucker, 1979) in Google Earth Engine (GEE).
NDVI is an index that varies between -1 and 1 that depicts the
greenness of vegetation which is often used to assess vegetation
prevalence. NDVI is calculated by utilizing the light reflection from
vegetation in the red and near-infrared wavelengths. We utilized the
Landsat 8 Surface Reflectance Tier 1 data product accessed through
GEE. This dataset is atmospherically corrected surface reflectance
from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared
Sensor (TIRS). Landsat 8 Surface Reflectance Tier 1 is available at
30x30-meter spatial resolution available every 16 days. Band 4 and
Band 5 represent the red and near-infrared spectral bands for Landsat
8. Images with zero percent cloud cover, as identified in the image
metadata, taken between the months of June through September from 2014
– 2016 were collected in GEE. These years closely align with the 2016
5-year American Community Survey (ACS) Dataset. While the 2016 5-year
ACS covers the years of 2012 and 2013, the Landsat 8 Surface
Reflectance Tier 1 data does not exist prior to 2014. A median
composite image was created from the set of filtered images. The
resulting composite image represents the median pixel values from the
filtered collection. NDVI was calculated for the composite image
calculated using the "normalizedDifference" function in
GEE. Finally, the average and standard deviation of NDVI were
calculated for each census tract in the Phoenix study area.
Once the variables were obtained for each respective index, both SoVI
and HVI were calculated using the same methodology. Using the psych
package (version 1.8.3.3) in R, we performed a principal components
analysis (PCA) on the Pearson product-moment correlation matrices of
the vulnerability variables. Many of the variables used in each index
are highly correlated; PCA eliminates issues with multicollinearity by
recombining total variance among the variables so that each resulting
component is uncorrelated with the other components. In an effort to
calculate SoVI and HVI in a manner that is most similar to how SoVI
and HVI are calculated and used by practitioners, we did not utilize
the interpretability of each component in our decision to retain or
not retain a given component. Instead, to determine the appropriate
number of components to extract, we primarily used the simple Kaiser
criterion of eigenvalues greater than one—as used by Cutter et al.
(2003) for SoVI and Harlan et al. (2013) for HVI. We then rotated the
retained components using varimax rotation to maintain independence
between components (orthogonality), distribute variance as evenly as
possible between components (the first components are still extracted
to retain maximal variance), and aid in interpretation.
Separately, for each index, we examined the variables that loaded most
highly on each component to determine the predominant directionality
of vulnerability (e.g. higher vulnerability vs. lower vulnerability)
captured by the component, using knowledge of how certain variables
influence vulnerability as identified in vulnerability and hazards
literature. The overall goal was to ensure that the cardinality of
each component was capturing the same phenomena, such that more
positive loadings represent higher vulnerability, and more negative
loadings represent lower vulnerability. In general, only component
loadings greater than .700 or less than -.700 were considered when
determining the directionality of vulnerability. However, when a
component only had smaller loadings, we considered variables with
loadings greater than .400 or less than -.400 to identify the
direction of vulnerability. For components that required a cardinality
correction, we multiplied the component scores by -1.
PCA of the 27 SoVI social vulnerability variables for Phoenix census
tracts yielded six components that accounted for 72% of the variance.
All but one of the components had multiple loadings greater than .700
or less than -.700 that agreed internally on the directionality of
vulnerability (e.g., variables loading highly on one component that
increase/decrease vulnerability all had the same cardinality within
that component). Incidentally, those same components were all output
with the correct cardinality, where positive loadings increase
vulnerability and negative loadings decrease vulnerability. Component
RC5 did not have any loadings greater than .700 or less than -.700 so
we used the smaller loadings to determine the direction of
vulnerability for RC5. The positive loadings for median dollar value
of owner-occupied housing units and percent of families earning more
than $200,000 per year, and the negative loadings for percent African
American indicated that RC5 should be adjusted for cardinality.
PCA of the 10 HVI heat vulnerability variables for Phoenix census
tracts yielded three components that accounted for 84% of the
variance. All three components had multiple loadings greater than .700
or less than -.700 that agreed internally on the directionality of
vulnerability. Component RC2 had positive loadings for vegetated area
mean and standard deviation, which required a cardinality adjustment
because increased vegetation is known to decrease heat vulnerability
(Harlan et al., 2013; Jenerette et al., 2016).
Lastly, we summed the indices’ component scores for each census tract
to produce a SoVI value that represents social vulnerability and an
HVI value that represents heat vulnerability of each census tract
relative to all other census tracts in Phoenix. We spatially joined
the SoVI and HVI index values to their respective census tract
shapefile using the tigris (version 0.7) and sf (version 0.7.2)
packages in R.
References:
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social
vulnerability to environmental hazards. Social Science Quarterly,
84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002
Harlan, S. L., Declet-Barreto, J. H., Stefanov, W. L., & Petitti,
D. B. (2013). Neighborhood effects on heat deaths: Social and
environmental predictors of vulnerability in Maricopa County, Arizona.
Environmental Health Perspectives, 121(2), 197–204.
https://doi.org/10.1289/ehp.1104625
Hazards & Vulnerability Research Institute (HVRI). (2014). SoVI®.
Retrieved December 5, 2018, from
http://artsandsciences.sc.edu/geog/hvri/sovi%C2%AE-0
Jenerette, G. D., Harlan, S. L., Buyantuev, A., Stefanov, W. L.,
Declet-Barreto, J., Ruddell, B. L., … Li, X. (2016). Micro-scale urban
surface temperatures are related to land-cover features and
residential heat related health impacts in Phoenix, AZ USA. Landscape
Ecology, 31(4), 745–760. https://doi.org/10.1007/s10980-015-0284-3
Reid, C. E., O’Neill, M. S., Gronlund, C. J., Brines, S. J., Brown, D.
G., Diez-Roux, A. V., & Schwartz, J. (2009). Mapping community
determinants of heat vulnerability. Environmental Health Perspectives,
117(11), 1730–1736. https://doi.org/10.1289/ehp.0900683
Tucker, C. J. (1979). Red and photographic infrared linear
combinations for monitoring vegetation. Remote Sensing of Environment,
8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0
